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CNN-RNN Algorithm-based Traffic Congestion Prediction System using Tri-Stage Attention

International Journal of Sensors, Wireless Communications and Control 2023年第2期13卷 89-98页

作者： Asif, S. Kartheeban, K. Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil Srivilliputtur 626126 India

Most people consider traffic congestion to be a major issue since it increases noise, pollu-tion, and time wastage. Traffic congestion is caused by dynamic traffic flow, which is a serious con-cern. The current normal traffic light system is not enough to handle the traffic congestion problems since it functions with a fixed-time length strategy. Methods: Despite the massive amount of traffic surveillance videos and images collected in daily monitoring, deep learning techniques for traffic intelligence management and control have been un-derutilized. Hence, in this paper, we propose a novel traffic congestion prediction system using a deep learning approach. Initially, the traffic data from the sensors is obtained and pre-processed using nor-malization. The features are extracted using Multi-Linear Discriminant Analysis (M-LDA). We propose Tri-stage Attention-based Convolutional Neural Network-Recurrent Neural Network (TA-CNN-RNN) for predicting traffic congestion. Results: To evaluate the effectiveness of the proposed model, the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) were used as the evaluation metrics. Conclusion: The experimental trial could extend its successful application to the traffic surveillance system and has the potential to enhance an intelligent transport system in the future. © 2023 Bentham science Publishers.

关键词： accuracy deep learning Multi-Linear Discriminant Analysis (M-LDA) prediction Traffic congestion Tri-stage Attention-based Convolutional Neural Network-Recurrent Neural Network (TA-CNN-RNN)

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Virtual reality based Neural Sensors with Respirator for Cardiorespiratory Synchrony Training System 9

Virtual reality based Neural Sensors with Respirator for Car...

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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Lu, Yun-En Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Liu, Lizheng Lin, Sheng Kai National Chen-Kung University Department of Computer Science and Information Engineering Taipei Taiwan National Central University Department of Computer Science and Information Engineering Taipei Taiwan Fudan University Academy for Engineering and Technology Shanghai China

ISBN: (纸本)9798350389500

Cardiorespiratory synchronization training (CRST) uses breathing to increase balance in the autonomic nervous system and reduce negative emotions. Virtual Reality (VR) makes the patients immersive in the virtual environment for CRST training system. All the participants were trained to reduce their breathing rates through slow and deep breathing to achieve their target resonance frequency. Our proposed to design the new system based on virtual reality technology, and combined with a variety of neural sensors, such as Looxid EEG sensor, BMD101 ECG sensor, and Arduino SFM3400 Respirator, not only embedded on CRST training system, but also machine learning method to stimulate a real relaxing environment. Develop the innovative features of clinical psychological intervention, user convenience and provide the therapist with real-time display of heart rate features(SDNN, LF, LnLf, HF, LF/HF, Mhr), detect emotion and flow data on the monitor. During the process, the computer calculates LnLf value and change VR scenes at any time, we provide an instant interactive system. Our showed the two modules of CRST training system including both of passive CRST module and direct CRST module, that heart rate variability (HRV) variables and EEG have significant difference between the first one minute and last one minute in CRST VR training system. © 2024 IEEE.

关键词： Electrocardiograms

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Comparative Analysis of Customised Neural Network Model and ML Classifiers for Brain Stroke Prediction

Comparative Analysis of Customised Neural Network Model and ...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Tejashree, S. Varshitha, S. Kodipalli, Ashwini Rao, Trupthi Kumarswamy, S. Global Academy of Technology Department of Cse Karnataka Bangalore India Global Academy of Technology Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

An abrupt interruption of the brain's blood supply leads to stroke, which causes severe damage to the brain. Due to this condition, there are people disabled and thus leading to death. In this work, the dataset contains ten features where environmental tests are done for the stroke which is utilized to evaluate the efficiency of Machine Learning classifiers and Deep Learning Artificial Neural Network. The purpose of this paper is to provide a forecast of the likelihood of a brain stroke occurring at an early stage using Machine Learning and Deep Learning techniques. The accuracy and efficiency of various types of Machine Learning models and customized Deep Learning models have been compared based on this impression. The custom Deep Learning model here used is an Artificial Neural Network with dropouts having the highest accuracy of 97.06% using RMSprop optimizer and a Decision Tree Machine Learning Classifier, achieved the highest accuracy of 95.98%. © 2023 IEEE.

关键词： Statistical tests

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Multi-Source Medical Data Integration and Mining for Healthcare Services 7

Multi-Source Medical Data Integration and Mining for Healthc...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Jeyaranjani, J. Prakash, Kodali Gnana Cheemakurthi, Sai Pavan Reddy, Banda Anoop Aashray, Lagishetty SRM Madurai College of Engineering and Technology Pottapalayam Computer Science and Engineering Sivagangai District India Kalasalingam Academy of Research and Education Anandnagar Computer Science and Engineering Krishnankoil India

ISBN: (纸本)9798350397253

As the Internet of Health (IoH) era has come into being, health services are progressively moving online and have been creating a sizable amount of health-related data related to patients, physicians, medical infrastructure, and other related topics. The effective integration and analysis of this IoH data have good implications for the provision of medical treatment and scientific disaster diagnosis. IoH data, however, are frequently dispersed throughout several departments and only partially protect user privacy. Therefore, efficiently integrating or mining the critical IoH data, while maintaining user privacy, is sometimes a difficult issue. Thus, this study provides IoH as a solution to this problem, allowing the IoH to submit medications to a specific patient without disclosing patient information and the admin to access the medications upon IoH's approval. Patients can watch physicians and consult them only once admin and the doctor have given their approval. © 2023 IEEE.

关键词： Data integration

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A Content-Based Filtering Approach for Personalized Movie Recommendations 2

A Content-Based Filtering Approach for Personalized Movie Re...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Vashisht, Rohit Sharma, Rahul Kumar Pant, Maneesh Thakral, Gagan Naresh, Prashant Department of Computer Science & Information Technology KIET Group of Institutions Ghaziabad India Department of Computer Science & Engineering KIET Group of Institutions Ghaziabad India Department of Information Technology JSS Academy of Technical Education Noida India

ISBN: (纸本)9798331530389

The recommendation system of today has made it simple to locate the necessities. Movie recommendation systems are designed to assist movie buffs by making recommendations for films to see, saving them the trouble and effort of having to sort through hundreds or even millions of options. The Recommender System has become a major area of research because it helps people find things online by making suggestions that are similar to what they are looking for. In this paper, we introduce a system called MOVIEHOPE for recommending movies. It uses a content-based filtering method that uses the user's preferences and the item's description to make a profile. In CBF, we use keywords to describe items instead of the user's profile to show what they like or do not like. In plain English, the CBF algorithm likes or recommends things that were liked in the past. It looks at how items have been rated before and suggests the best item that fits. © 2025 IEEE.

关键词： Collaborative filtering

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Lungs Disease Classification using VGG-16 architecture with PCA

Lungs Disease Classification using VGG-16 architecture with ...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Gupta, Vaishali Patel, Ruchi Institute of Engineering and Science Department of Computer Science and Engineering IPS Academy MP Indore India Gyan Ganga Institute of Technology and Sciences Department of Computer Science and Engineering MP Jabalpur India

ISBN: (纸本)9798350396485

People all over the world are afflicted by lung disease, which is a prevalent illness. The earliest possible diagnosis of lung illness is necessary. Due to this, a number of deep learning models for processing image data evolved over time. Advances in deep learning have helped identify lung disorders and detect them in diagnostic images. Various types of modern deep learning techniques, including vanilla neural networks, convolutional neural networks (CNN), visual geometry group (VGG) dependent neural networks, and capsule networks, can be used to classify lung cancer. The basic CNN performs poorly when trying to handle rotated, curved, or other unusual image orientations. As a result, the proposed work explored using principal components analysis (PCA) and the VGG16 deep learning architecture. In order to extract significant features from an image dataset, PCA is generally used. The Chest X-ray of National Institutes of Health (NIH) is taken as dataset which contains 112,120 images of X-ray of 30,805 different patients. In the current work, accuracy is used to evaluate performance, and VGG 16's accuracy is 79.1%. The PCA approach has raised it by up to 96%. Additionally, the proposed architecture is contrasted with current work. © 2023 IEEE.

关键词： Principal component analysis

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An Effective Counterfeit Medicine Authentication System Using Blockchain and IoT 4

An Effective Counterfeit Medicine Authentication System Usin...

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4th International Conference for Emerging technology, INCET 2023

作者： Shalini, S. Sheela, S. Abhishek, S. Bhavyashree, P. Gunashree, C. Rohan, K.S. VTU University Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350335750

The current pharmaceutical sector is dealing with several issues. The pharmaceutical sector nowadays is dominated by counterfeit medications. Health research funding groups estimate that between 10 and 30 percent of the pharmaceuticals in the market are counterfeited. Globally, this fatal issue is creating several serious health risks. WHO estimates that 30% of all medications worldwide are fake. In practically every region of the world, this issue is becoming worse by the second. As drugs travel across several scattered networks, it is challenging to identify fakes. By integrating Blockchain technology into the network, the pharmaceutical supply chain may be made safer. Every area of the network keeps track of the medications through a record, making it possible to identify any introduction of fake medications. System transparency and dependability are improved with Blockchain security. By employing Blockchain technology, the initiative seeks to guarantee medicine quality, transaction security, and data protection. By eliminating information about expired and fake medications from the system, this effort increases system dependability and helps to make the Blockchain a stable and widely used technology. To determine whether or not counterfeit medications have been distributed, IOT technology is also employed. © 2023 IEEE.

关键词： Smart contract

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Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices

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National science Review 2022年第11期9卷 172-181页

作者： Shufang Zhao Wenhao Ran Zheng Lou Linlin Li Swapnadeep Poddar Lili Wang Zhiyong Fan Guozhen Shen State Key Laboratory for Superlattices and Microstructures Institute of SemiconductorsChinese Academy of Sciences and Center of Materials Science and Optoelectronic EngineeringUniversity of Chinese Academy of Sciences Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology School of Integrated Circuits and Electronics Beijing Institute of Technology

High-accuracy neuromorphic devices with adaptive weight adjustment are crucial for high-performance computing. However, limited studies have been conducted on achieving selective and linear synaptic weight updates without changing electrical pulses. Herein, we propose high-accuracy and self-adaptive artificial synapses based on tunable and flexible MXene energy storage devices. These synapses can be adjusted adaptively depending on the stored weight value to mitigate time and energy loss resulting from recalculation. The resistance can be used to effectively regulate the accumulation and dissipation of ions in single devices, without changing the external pulse stimulation or preprogramming, to ensure selective and linear synaptic weight updates. The feasibility of the proposed neural network based on the synapses of flexible energy devices was investigated through training and machine learning. The results indicated that the device achieved a recognition accuracy of ～95% for various neural network calculation tasks such as numeric classification.

关键词： neuromorphic computing MXene flexible device high accuracy adaptability ion dynamics

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Evaluating and Contrasting the Performance of Web Development Technologies 9

Evaluating and Contrasting the Performance of Web Developmen...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Swedheetha, C. Shruthika, C. Preethi, Rubi Vedha Varshini, T.B.J. Maryam, Sabreen Manikandan, P. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350377972

The next generation of webpages is heavily influenced by large scale, high concurrency, and massive amounts of data. Building data-intensive web apps has made *** popular and effective. We employed scenario testing and benchmark testing to examine and contrast the performance of ***, Python-Web, and PHP. Per the testing results, PHP and Python-Web handle a lot less requests in a given amount of time than ***, which is evident from some useful performance metrics. Our findings, taken together, show that *** is the most lightweight and efficient of the three, making it a good choice for I/O-intensive websites;PHP, on the other hand, is best suited for small- to medium-sized applications, while Python-Web is developer-friendly and excels at large web architectures. This research study includes the performance evaluation of web development technologies. © 2024 IEEE.

关键词： Python

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An Improved Deadlock Recovery Policy of Flexible Manufacturing Systems Based on Resource Flow Graphs

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IEEE Access 2024年 12卷 65202-65212页

作者： Tseng, Ching-Yun Chen, Ju-Chin Pan, Yen-Liang National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan R.O.C. Air Force Academy Mathematics and Physics Division General Education Center Gangshan Kaohsiung820009 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung807618 Taiwan Chaoyang University of Technology Department of Aeronautical Engineering Taichung413310 Taiwan

The system deadlock problem of the flexible manufacturing system (FMS) needs to be solved urgently. Deadlocks may occur when resources are repeatedly requested by multiple working processes, where such competition causes a lack of resources, and the system cannot get into subsequent processes smoothly. In this paper, we build FMS by using a particular class of Petri Net (PN) model called a system of simple sequential processes with resources (S3PR) net, which features a high utilization rate of resources. When parts of resources are held by related processes and these processes wait for others to release resources to move on, a circular waiting situation occurs. Circular waiting is one of the conditions for system deadlocks occurring, where the system under this situation has a high probability of causing system deadlocks. The resources being held have to be released to break the waiting states. This paper develops a novel control transition-based deadlock recovery policy by combining graph theory and reachability graph analysis technique. The proposed approach firstly builds a subnet of the PN model called resource flow graph (RFG) and finds out all its hold and request circuits (HRCs), which may lead to a circular waiting situation. In addition, we design a new algorithm to obtain the minimal set of control transitions further. We introduce three PN models of FMS to demonstrate the proposed method. Through experimental results and comparison between related works, it is known that the proposed deadlock recovery uses fewer controllers and has the same performance. © 2013 IEEE.

关键词： Frequency modulation

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